AI Workflow: AI in Production

Description

In this course, you will :

• Deploy a flask application using Docker.
• Create a simple user interface (UI) to integrate the ML model, Watson NLU, and Watson Visual Recognition.
• Go over the fundamentals of Kubernetes terminology.
• Use Kubernetes to deploy a scalable web application.
• Discuss the various feedback loops in an AI workflow.
• Discuss the application of unit testing in the context of model development.
• Assess the bias and performance of production machine learning models using IBM Watson OpenScale.

Syllabus :

1. Feedback loops and Monitoring

• Feedback Loops and Unit Testing
• Feedback Loops and Unit Tests
• Performance Monitoring and Business Metrics
• Performance Drift

 

2. Hands on with Openscale and Kubernetes

• Operationalize Trusted AI with IBM Watson OpenScale
• Kubernetes Explained
• Kubernetes vs. Docker: It's Not an Either/Or Question

 

3. Capstone: Pulling it all together (Part 1)

• What is in the Capstone and Associated Review?
• Review of Course 1: Business Priorities and Data Ingestion
• Review of Course 2: Data Analysis and Hypothesis Testing
• Review of Course 3: Feature Engineering and Bias Detection
• Review of Course 4: Machine Learning, Visual Recognition, and NLP
• Review of Course 5: Enterprise Model Deployment
• About the Data

 

4. Capstone: Pulling it all together (Part 2)

• Capstone Assignment 2: Through the Eyes of Our Working Example
• Capstone Part 2: Getting Started (Hands-On)
• Capstone Part 3: Getting Started (Hands-On)
• Solution Files

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